ColiCoords: A Python package for the analysis of bacterial fluorescence microscopy data

Quantitative microbiology with widefield microscopy: navigating optical artefacts for accurate interpretations

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows researchers to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as their dimensions approache that of the microscope's depth of field, and they begin to experience significant diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employed simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size, fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning-based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

Contribution of different macromolecules to the diffusion of a 40 nm particle in Escherichia coli

Due to the high concentration of proteins, nucleic acids, and other macromolecules, the bacterial cytoplasm is typically described as a crowded environment. However, the extent to which each of these macromolecules individually affects the mobility of macromolecular complexes, and how this depends on growth conditions, is presently unclear. In this study, we sought to quantify the crowding experienced by an exogenous 40 nm fluorescent particle in the cytoplasm of E. coli under different growth conditions. By performing single-particle tracking measurements in cells selectively depleted of DNA and/or mRNA, we determined the contribution to crowding of mRNA, DNA, and remaining cellular components, i.e., mostly proteins and ribosomes. To estimate this contribution to crowding, we quantified the difference of the particle's diffusion coefficient in conditions with and without those macromolecules. We found that the contributions of the three classes of components were of comparable magnitude, being largest in the case of proteins and ribosomes. We further found that the contributions of mRNA and DNA to crowding were significantly larger than expected based on their volumetric fractions alone. Finally, we found that the crowding contributions change only slightly with the growth conditions. These results reveal how various cellular components partake in crowding of the cytoplasm and the consequences this has for the mobility of large macromolecular complexes.

Findaureus: An open-source application for locating Staphylococcus aureus in fluorescence-labelled infected bone tissue slices

Staphylococcus aureus (S. aureus) is a facultative pathogenic bacterium that can cause infections in various tissue types in humans. Fluorescence imaging techniques have been employed to visualize the bacteria in ex-vivo samples mostly in research, aiding in the understanding of the etiology of the pathogen. However, the multispectral images generated from fluorescence microscopes are complex, making it difficult to locate bacteria across image files, especially in consecutive planes with different imaging depths. To address this issue, we present Findaureus, an open-source application specifically designed to locate and extract critical information about bacteria, especially S. aureus. Due to the limited availability of datasets, we tested the application on a dataset comprising fluorescence-labelled infected mouse bone tissue images, achieving an accuracy of 95%. We compared Findaureus with other state-of-the-art image analysis tools and found that it performed better, given its specificity toward bacteria localization. The proposed approach has the potential to aid in medical research of bone infections and can be extended to other tissue and bacteria types in the future.

Simulation-based Reconstructed Diffusion unveils the effect of aging on protein diffusion in Escherichia coli

We have developed Simulation-based Reconstructed Diffusion (SbRD) to determine diffusion coefficients corrected for confinement effects and for the bias introduced by two-dimensional models describing a three-dimensional motion. We validate the method on simulated diffusion data in three-dimensional cell-shaped compartments. We use SbRD, combined with a new cell detection method, to determine the diffusion coefficients of a set of native proteins in Escherichia coli. We observe slower diffusion at the cell poles than in the nucleoid region of exponentially growing cells, which is independent of the presence of polysomes. Furthermore, we show that the newly formed pole of dividing cells exhibits a faster diffusion than the old one. We hypothesize that the observed slowdown at the cell poles is caused by the accumulation of aggregated or damaged proteins, and that the effect is asymmetric due to cell aging.

Synthetic Micrographs of Bacteria (SyMBac) allows accurate segmentation of bacterial cells using deep neural networks

BACKGROUND

Deep-learning-based image segmentation models are required for accurate processing of high-throughput timelapse imaging data of bacterial cells. However, the performance of any such model strictly depends on the quality and quantity of training data, which is difficult to generate for bacterial cell images. Here, we present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac).

RESULTS

We have developed SyMBac, a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs, and is capable of training accurate image segmentation models. The major advantages of our approach are as follows: (1) synthetic training data can be generated virtually instantly and on demand; (2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; (3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in one's experimental setup no longer requires the laborious process of manually generating new training data for each change. Deep-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate cell identifications and precise cell masks than those trained on human-annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. We illustrate the approach by analysing the growth and size regulation of bacterial cells during entry and exit from dormancy, which revealed novel insights about the physiological dynamics of cells under various growth conditions.

CONCLUSIONS

The SyMBac approach will help to adapt and improve the performance of deep-learning-based image segmentation models for accurate processing of high-throughput timelapse image data.

Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation

Advances in microscopy hold great promise for allowing quantitative and precise measurement of morphological and molecular phenomena at the single-cell level in bacteria; however, the potential of this approach is ultimately limited by the availability of methods to faithfully segment cells independent of their morphological or optical characteristics. Here, we present Omnipose, a deep neural network image-segmentation algorithm. Unique network outputs such as the gradient of the distance field allow Omnipose to accurately segment cells on which current algorithms, including its predecessor, Cellpose, produce errors. We show that Omnipose achieves unprecedented segmentation performance on mixed bacterial cultures, antibiotic-treated cells and cells of elongated or branched morphology. Furthermore, the benefits of Omnipose extend to non-bacterial subjects, varied imaging modalities and three-dimensional objects. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism. Our results distinguish Omnipose as a powerful tool for characterizing diverse and arbitrarily shaped cell types from imaging data.

High-Throughput Imaging of Bacillus subtilis

Bacillus subtilis is a widely used model bacterium to study cellular processes and development. The availability of an arrayed mutant library gave us the opportunity to cytologically analyze every mutant and screen for new genes involved in cell shape determination, cell division, and chromosome segregation. Here we describe a high-throughput method to image arrayed B. subtilis mutant libraries using wide-field fluorescence microscopy. We provide a detailed description of growing the arrayed strain collection, preparing slides containing agarose pedestals, setting up the microscopy procedure, acquiring images, and analyzing the images.

Structural Dynamics of the Functional Nonameric Type III Translocase Export Gate

Type III protein secretion is widespread in Gram-negative pathogens. It comprises the injectisome with a surface-exposed needle and an inner membrane translocase. The translocase contains the SctRSTU export channel enveloped by the export gate subunit SctV that binds chaperone/exported clients and forms a putative ante-chamber. We probed the assembly, function, structure and dynamics of SctV from enteropathogenic E. coli (EPEC). In both EPEC and E. coli lab strains, SctV forms peripheral oligomeric clusters that are detergent-extracted as homo-nonamers. Membrane-embedded SctV₉ is necessary and sufficient to act as a receptor for different chaperone/exported protein pairs with distinct C-domain binding sites that are essential for secretion. Negative staining electron microscopy revealed that peptidisc-reconstituted His-SctV₉ forms a tripartite particle of ∼22 nm with a N-terminal domain connected by a short linker to a C-domain ring structure with a ∼5 nm-wide inner opening. The isolated C-domain ring was resolved with cryo-EM at 3.1 Å and structurally compared to other SctV homologues. Its four sub-domains undergo a three-stage "pinching" motion. Hydrogen-deuterium exchange mass spectrometry revealed this to involve dynamic and rigid hinges and a hyper-flexible sub-domain that flips out of the ring periphery and binds chaperones on and between adjacent protomers. These motions are coincident with local conformational changes at the pore surface and ring entry mouth that may also be modulated by the ATPase inner stalk. We propose that the intrinsic dynamics of the SctV protomer are modulated by chaperones and the ATPase and could affect allosterically the other subunits of the nonameric ring during secretion.

Development of the automated software and device for determination of wicking in textiles using open-source tools

The development of automated software and the device for determination of wicking of textile materials, using open-source ImageJ libraries for image processing, and newly designed additional algorithm for the determination of threshold, is presented in this paper. The description of the device, design of the open-source software “Kapilarko”, as well as an explanation of the steps: image processing, threshold determination and reading of wicking height, are provided. We have also investigated the possibility of using the artificial neural networks for automatic recognition of the wicking height. The results showed that the recognition of the wet area of the sample, based on the application of artificial neural networks was in a very good agreement with the experimental data. The device's utility for the measurement of wicking ability of textile materials was proved by testing various knitted fabrics. The constructed device has the advantages of providing automated measurement and minimization of the subjective errors of the operators; extremely fast or long-term measurements; digital recording of results; consistency of experimental conditions; possibility of using water instead of colors and, last but not least, low cost of the device. Considering the importance and frequent measurements of wicking ability of textile materials, the advantages of the presented device, as well as the fact that commercial software without publishing the source-code, are used for most of the available devices, we believe that our idea to design the automated software and device by applying the "open-source" approach, will be of benefit to scientists and engineers in using or improving wicking experiments.